> ML_LITERATURE // ABADI-2016-TENSORFLOW-LARGE-SCALE-MACHINE-LEARNING-HETEROGENEOUS-SYSTEMS_v1.0
TensorFlow: A System for Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng · USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2016)
systems2016industry-standardartifactsAvailable
Principal Contribution
Dataflow graph execution architecture mapping tensor computations across heterogeneous hardware devices from smartphones to TPU pods.
Operational Relevance
Directly guides deployment choices and architecture selection for task-distributed-training, task-production-serving.
Assumptions
- Standard empirical regularity and statistical stability hold across evaluation domains
Limitations
- Performance characteristics depend on domain distribution and compute allocation parameters
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: